Journal: The journal of physical chemistry letters
Chalcogenide perovskites with optimal bandgap and desirable light absorption are promising for photovoltaic devices, whereas the absence of ferroelectricity limits their potential in application. Based on first-principles calculations, we reveal the underlying mechanism of the paraelectric nature of Ba3Zr2S7 observed in experiments and demonstrate a general rule for the appearance of ferroelectricity in chalcogenide perovskites with Ruddlesden-Popper (RP) A3B2X7 structures. Group theoretical analysis shows that tolerance factor is the primary factor that dominates the ferroelectricity. Both Ba3Zr2S7 and Ba3Hf2S7 with large tolerance factors are paraelectric due to the suppression of in-phase rotation that is indispensable to hybrid improper ferroelectricity. In contrast, Ca3Zr2S7, Ca3Hf2S7, Ca3Zr2Se7 and Ca3Hf2S7 with small tolerance factors exhibit in-phase rotation and can be stable in the ferroelectric Cmc21 ground state with non-trivial polarization. These findings not only provide useful guidance to engineering ferroelectricity in RP chalcogenide perovskites but also suggest potential ferroelectric semiconductors for photovoltaic applications.
Despite many recent developments in designing and screening catalysts for improved performance, transition-metal oxides continue to prove challenging due to the myriad inherent complexities of the systems and the possible morphologies that it can exhibit. Herein we propose a structural descriptor, the adjusted coordination number (ACN), which can generalize the reactivity of the oxygen sites over the many possible surface facets and defects of a transition-metal oxide. We demonstrate the strong correlation of this geometric descriptor with the electronic and energetic properties of the active sites on five facets of four transition-metal oxides. We then use the structural descriptor to predict C-H activation energies, to show the great potential of using ACN for the prediction of catalytic performance. This study presents a first look into quantifying the relation between active site structure and reactivity of transition-metal-oxide catalysts.
The surface tension of water is an important parameter for many biological or industrial processes, and roughly a factor of three higher than that of non-polar liquids such as oils, which is usually attributed to hydrogen bonding and dipolar interactions. Here we show by studying the formation of water drops that the surface tension of a freshly created water surface is even higher (~ 90 mNm(-1)) than under equilibrium conditions (~ 72 mNm(-1)) with a relaxation process occurring on a long timescale (~ 1 ms). Dynamic adsorption effects of protons or hydroxides may be at the origin of this dynamic surface tension. However, changing the pH does not significantly change the dynamic surface tension. It also seems unlikely that hydrogen bonding or dipole orientation effects play any role at the relatively long time scale probed in the experiments.
Using an X-ray laser, we investigated the crystal structure of ice formed by homogeneous ice nucleation in deeply supercooled water nanodrops (r ~10 nm) at ~225 K. The nanodrops were formed by condensation of vapor in a supersonic nozzle, and the ice was probed within 100 µs of freezing using femtosecond wide angle X-ray scattering at the Linac Coherent Light Source free-electron X-ray laser. The X-ray diffraction spectra indicate the ice has a metastable, predominantly cubic structure; the shape of the first ice diffraction peak suggests stacking-disordered ice with a cubicity value, χ, in the range of 0.78 ± 0.05. The cubicity value determined here is higher than those determined in experiments with micron-sized drops, but comparable to those found in molecular dynamics simulations. The high cubicity is most likely caused by the extremely low freezing temperatures, and by the rapid freezing, which occurs on a ~1 µs timescale in single nanodroplets.
Fluorescent proteins (FPs) are indispensable markers for two-photon imaging of live tissue, especially in the brains of small model organisms. The quantity of physiologically relevant data collected, however, is limited by heat-induced damage of the tissue due to the high intensities of the excitation laser. We seek to minimize this damage by developing FPs with improved brightness. Among FPs with the same chromophore structure, the spectral properties can vary widely due to differences in the local protein environment. Using a physical model that describes the spectra of FPs containing the anionic green FP (GFP) chromophore, we predict that those that are blue-shifted in one-photon absorption will have stronger peak two-photon absorption cross sections. Following this prediction, we present 12 blue-shifted GFP homologs and demonstrate that they are up to 2.5 times brighter than the commonly used enhanced GFP (EGFP).
Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstrate prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. In addition, the same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies.
Tracking the structure of heterogeneous catalysts under operando conditions remains a challenge due to the paucity of experimental techniques that can provide atomic-level information for catalytic metal species. Here we report on the use of X-ray absorption near edge structure (XANES) spectroscopy and supervised machine learning (SML) for refining the three-dimensional geometry of metal catalysts. SML is used to unravel the hidden relationship between the XANES features and catalyst geometry. To train our SML method, we rely on ab-initio XANES simulations. Our approach allows one to solve the structure of a metal catalyst from its experimental XANES, as demonstrated here by reconstructing the average size, shape and morphology of well-defined platinum nanoparticles. This method is applicable to the determination of the nanoparticle structure in operando studies and can be generalized to other nanoscale systems. It also allows on-the-fly XANES analysis, and is a promising approach for high-throughput and time-dependent studies.
Super-resolution microscopy typically achieves high spatial resolution, but the temporal resolution remains low. We report super temporal-resolved microscopy (STReM) to improve the temporal resolution of 2D super-resolution microscopy by a factor of 20 compared to that of the traditional camera-limited frame rate. This is achieved by rotating a phase mask in the Fourier plane during data acquisition and then recovering the temporal information by fitting the point spread function (PSF) orientations. The feasibility of this technique is verified with both simulated and experimental 2D adsorption/desorption and 2D emitter transport. When STReM is applied to measure protein adsorption at a glass surface, previously unseen dynamics are revealed.
Basing on ab initio density functional calculations, we performed a comprehensive investigation of the general graphitization tendency in rocksalt-type structures. In this paper, we determine the critical slab thickness for a range of ionic cubic crystal systems, below which a spontaneous conversion from a cubic to a layered graphitic-like structure occurs. This conversion is driven by surface energy reduction. Using only fundamental parameters of the compounds such as the Allen electronegativity and ionic radius of the metal atom, we also develop an analytical relation to estimate the critical number of layers.
Structuring caused by the mixing of liquids or the addition of solutes to a solvent causes the viscosity to increase. The classical example is mayonnaise: a mixture of two low viscosity liquids, water and oil, is structured through the addition of a surfactant creating a dispersed phase, causing the viscosity to increase a thousandfold. The dramatic increase in viscosity in highly concentrated solutions is a long-standing unsolved problem in physical chemistry. Here we will show that this viscosity increase can be understood in terms of the solute-induced structuring of the first solvation shell leading to a jamming transition at a critical concentration. As the jamming transition is approached, the viscosity naturally increases according to a Vogel-Fulcher-Tammann type expression. This result calls into question the validity of the Jones-Dole B-coefficient as an indicator of structure making or breaking ability of solutes.